Ding Hongwei, Liu Yuting, Wang Zongshan, Jin Gushen, Hu Peng, Dhiman Gaurav
School of Information Science and Engineering, Yunnan University, Kunming 650106, China.
Glasgow College, University of Electronic Science and Technology of China, Chengdu 611731, China.
Biomimetics (Basel). 2023 Aug 23;8(5):383. doi: 10.3390/biomimetics8050383.
The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these shortcomings, an enhanced EO algorithm is proposed in this paper. First, a spiral search mechanism is introduced to guide the particles to more promising search regions. Then, a new inertia weight factor is employed to mitigate the oscillation phenomena of particles. To evaluate the effectiveness of the proposed algorithm, it has been tested on the CEC2017 test suite and the mobile robot path planning (MRPP) problem and compared with some advanced metaheuristic techniques. The experimental results demonstrate that our improved EO algorithm outperforms the comparison methods in solving both numerical optimization problems and practical problems. Overall, the developed EO variant has good robustness and stability and can be considered as a promising optimization tool.
平衡优化器（EO）是一种最近开发的基于物理的优化技术，用于解决复杂的优化问题。尽管该算法显示出优异的开发能力，但它仍然存在一些缺点，例如容易陷入局部最优以及种群多样性较差。为了解决这些缺点，本文提出了一种改进的EO算法。首先，引入了一种螺旋搜索机制，以引导粒子进入更有希望的搜索区域。然后，采用了一种新的惯性权重因子来减轻粒子的振荡现象。为了评估所提算法的有效性，在CEC2017测试套件和移动机器人路径规划（MRPP）问题上对其进行了测试，并与一些先进的元启发式技术进行了比较。实验结果表明，我们改进的EO算法在解决数值优化问题和实际问题方面均优于比较方法。总体而言，所开发的EO变体具有良好的鲁棒性和稳定性，可被视为一种有前途的优化工具。
